HIGH HIT-RATE ORACLE - 1Z0-1127-25 - NEW ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL EXAM QUESTIONS

High Hit-Rate Oracle - 1Z0-1127-25 - New Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Questions

High Hit-Rate Oracle - 1Z0-1127-25 - New Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Questions

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
How are chains traditionally created in LangChain?

  • A. Exclusively through third-party software integrations
  • B. By using machine learning algorithms
  • C. Using Python classes, such as LLMChain and others
  • D. Declaratively, with no coding required

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Traditionally, LangChain chains (e.g., LLMChain) are created using Python classes that define sequences of operations, such as calling an LLM or processing data. This programmatic approach predates LCEL's declarative style, making Option C correct. Option A is vague and incorrect, as chains aren't ML algorithms themselves. Option B describes LCEL, not traditional methods. Option D is false, as third-party integrations aren't required. Python classes provide structured chain building.
OCI 2025 Generative AI documentation likely contrasts traditional chains with LCEL under LangChain sections.


NEW QUESTION # 51
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

  • A. "Top p" determines the maximum number of tokens per response.
  • B. "Top p" assigns penalties to frequently occurring tokens.
  • C. "Top p" limits token selection based on the sum of their probabilities.
  • D. "Top p" selects tokens from the "Top k" tokens sorted by probability.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.


NEW QUESTION # 52
What is the purpose of memory in the LangChain framework?

  • A. To act as a static database for storing permanent records
  • B. To store various types of data and provide algorithms for summarizing past interactions
  • C. To perform complex calculations unrelated to user interaction
  • D. To retrieve user input and provide real-time output only

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.


NEW QUESTION # 53
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

  • A. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
  • B. A given StreamlitChatMessageHistory will not be shared across user sessions.
  • C. A given StreamlitChatMessageHistory will NOT be persisted.
  • D. StreamlitChatMessageHistory can be used in any type of LLM application.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
StreamlitChatMessageHistory integrates with Streamlit's session state to store chat history, tied to a specific key (Option A, true). It's not persisted beyond the session (Option B, true) and isn't shared across users (Option C, true), as Streamlit sessions are user-specific. However, it's designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true.
OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.


NEW QUESTION # 54
What is the function of the Generator in a text generation system?

  • A. To generate human-like text using the information retrieved and ranked, along with the user's original query
  • B. To rank the information based on its relevance to the user's query
  • C. To store the generated responses for future use
  • D. To collect user queries and convert them into database search terms

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that produces coherent, human-like text based on the user's query and any retrieved information (if applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever's role. Option B pertains to a Ranker. Option D is unrelated, as storage isn't the Generator's function but a separate system task. The Generator's role is critical in transforming inputs into natural language responses.
OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation workflows.


NEW QUESTION # 55
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